Automatic parameter estimation of multicompartmental neuron models via minimization of trace error with control adjustment
Automatic parameter estimation of multicompartmental neuron models via minimization of trace error with control adjustment
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DOI：
10.1152/jn.00007.2014
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发表时间：
2014-11-01
影响因子：
2.5
通讯作者：
Marder, Eve
中科院分区：
文献类型：
--
作者：
Brookings, Ted;Goeritz, Marie L.;Marder, Eve
我们描述了一种新的技术，以适应电导为基础的神经元模型，从孤立的生物神经元的细胞内电压迹线。在电流钳中记录生物神经元，并注入粉红色（1/f）噪声以干扰神经元的活动。新算法找到一组参数，允许多房室模型神经元匹配记录的电压轨迹。试图直接匹配记录的电压迹线有一个众所周知的问题：生物和模型神经元之间的动作电位时序不匹配是不可避免的，并导致模型和数据之间的现象学匹配不良。我们的方法通过对模型应用弱控制调整来避免这种情况，以在拟合过程中促进对齐。这种方法与Luenberger观测器的控制理论概念密切相关。我们测试了这种方法的合成数据和数据记录从前胃受体神经元的口胃神经节的螃蟹癌症borealis。为了测试这种方法的灵活性，合成数据构建与电导模型，这是不同的拟合模型中使用的。对于合成和生物数据，所得模型具有良好的尖峰定时精度。
We describe a new technique to fit conductance-based neuron models to intracellular voltage traces from isolated biological neurons. The biological neurons are recorded in current-clamp with pink (1/f) noise injected to perturb the activity of the neuron. The new algorithm finds a set of parameters that allows a multicompartmental model neuron to match the recorded voltage trace. Attempting to match a recorded voltage trace directly has a well-known problem: mismatch in the timing of action potentials between biological and model neuron is inevitable and results in poor phenomenological match between the model and data. Our approach avoids this by applying a weak control adjustment to the model to promote alignment during the fitting procedure. This approach is closely related to the control theoretic concept of a Luenberger observer. We tested this approach on synthetic data and on data recorded from an anterior gastric receptor neuron from the stomatogastric ganglion of the crab Cancer borealis. To test the flexibility of this approach, the synthetic data were constructed with conductance models that were different from the ones used in the fitting model. For both synthetic and biological data, the resultant models had good spike-timing accuracy.